gospelgit
/

African-Languages-Sentiment-Classifier

@@ -1,202 +1,125 @@
 ---
 base_model: togethercomputer/gpt-oss-20b-bf16
 library_name: peft
 ---
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-### Framework versions
-- PEFT 0.15.1

 ---
 base_model: togethercomputer/gpt-oss-20b-bf16
 library_name: peft
+license: cc-by-4.0
+language:
+- ha
+- yo
+- sw
+tags:
+- sentiment-analysis
+- african-languages
+- lora
+- peft
+- autoscientist-challenge
 ---
+# African Languages Sentiment Classifier (Hausa, Yorùbá, Swahili)
+A LoRA-adapted sentiment classifier for Hausa, Yorùbá, and Swahili, fine-tuned
+on [`gospelgit/African-Languages_Sentiments`](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments)
+— a combined dataset of **46,725 rows** stitched from three independent
+sources across three different domains, built to reduce the single-domain
+(Twitter-only) bias common in existing African-language sentiment resources.
 ## Model Details
+- **Base model:** `togethercomputer/gpt-oss-20b-bf16`
+- **Adapter type:** LoRA (PEFT), rank 64, alpha 128, target modules `q_proj`/`k_proj`/`v_proj`/`o_proj`
+- **Task formulation:** causal LM, prompt → single-word completion (the model generates the sentiment label as its next-token completion)
+- **Languages:** Hausa, Yorùbá, Swahili
+- **License:** CC-BY-4.0
+- **Produced via:** [Adaption Labs AutoScientist](https://adaptionlabs.ai/blog/autoscientist-challenge) (Language category submission)
+- **AutoScientist training run ID:** `adaption_gpt_oss_20b_ha_yo_sw_sentiment_1eb424c7`
+## Training Data
+The full dataset card, source breakdown, and licensing details live at
+[`gospelgit/African-Languages_Sentiments`](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments).
+Summary:
+| Source | Domain | Languages | Rows |
+|---|---|---|---|
+| AfriSenti | Twitter | Hausa, Yorùbá, Swahili | 40,290 |
+| NollySenti | Nollywood movie reviews (human-translated) | Hausa, Yorùbá | 2,510 |
+| Neurotech-HQ Swahili | Social media / product reviews (back-translated) | Swahili | 3,925 |
+3-class labels (`positive` / `negative` / `neutral`), 70/15/15 train/dev/test
+split per language, stratified by label.
+> **Note on training data adaptation**: this specific adapter was trained on
+> an AutoScientist-evolved version of the dataset above — its "Adaptive
+> Data" step rewrote the original rows into `enhanced_prompt`/
+> `enhanced_completion` pairs (15,280 rows after this process) as part of
+> its data-and-recipe co-optimization loop. Both versions are available in
+> the [`gospelgit/African-Languages_Sentiments`](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments)
+> repo: the original combined dataset described above, and the
+> AutoScientist-adapted version this model was actually trained on. If you
+> want the raw, unmodified rows for your own training pipeline, use the
+> original files rather than the adapted ones.
+## Training Procedure
+- 5 epochs, 585 total steps
+- Train/eval loss decreased steadily across all 5 epochs (eval loss:
+  0.828 → 0.787 → 0.769 → 0.760 → 0.757)
+- Learning rate: warm-up then decay schedule
+- Framework: PEFT 0.15.1
+## Evaluation (AutoScientist internal metrics)
+These are AutoScientist's own judge-based scores comparing the base model
+against the fine-tuned ("adapted") model — **not standard accuracy/F1**:
+| Metric | Before (base) | After (adapted) |
+|---|---|---|
+| Quality score (0–10 scale) | 3.0 | 6.9 (+130% relative) |
+| Grade | E | C |
+| Percentile | 1.3 | 8.4 |
+| Win rate — on this dataset | 44 | 57 |
+| Win rate — general category (all tasks) | 52 | 48 |
+**Read this table carefully**: task-specific quality improved substantially
+(grade E→C, +130% relative quality score), but the general-category win rate
+slightly *dropped* (52→48), meaning the adaptation traded a small amount of
+general-purpose capability for sentiment-task performance. This is disclosed
+deliberately — don't assume "adapted" is strictly better in every dimension.
+## Intended Use
+Sentiment classification (positive/negative/neutral) for short-form text in
+Hausa, Yorùbá, or Swahili, primarily for research and benchmarking purposes
+within the AutoScientist Challenge. Not validated for production deployment.
+## How to Use
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/gpt-oss-20b-bf16")
+model = PeftModel.from_pretrained(base_model, "gospelgit/African-Languages-Sentiment-Classifier")
+tokenizer = AutoTokenizer.from_pretrained("gospelgit/African-Languages-Sentiment-Classifier")
+prompt = "Classify the sentiment of this text as positive, negative, or neutral: <your text here>"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=5)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+## Limitations
+- Evaluated via AutoScientist's internal judge/win-rate system, not an
+  external, reproducible benchmark — independent verification is
+  recommended before relying on these numbers.
+- Trained on an evolved/rewritten version of the source data, not the raw
+  human-annotated labels directly.
+- Slight general-capability regression observed post-adaptation (see table
+  above).
+- Swahili has less underlying data than Hausa/Yorùbá — performance may be
+  less stable for that language.
+## Citation
+If you use this model, please also cite the original dataset sources
+listed in the [dataset card](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments).